Tools in Metabonomics: An Integrated Validation Approach for LC-MS Metabolic Profiling of Mercapturic Acids in Human Urine

Fecal metabolomics combined with metagenomics sequencing to analyze the antidepressant mechanism of Yueju Wan

BACKGROUND

Yueju Wan (YJW), defined in Danxi's Mastery of Medicine, has Qi-regulating and Qi-promoting effects. YJW has frequently been applied in the clinic for the treatment of depression. Substantial evidence has shown that depression is related to metabolic abnormalities of the gut microbiota, and traditional Chinese medicine (TCM) can treat depression by adjusting gut microbiota metabolism. The antidepressant effect of YJW is well established, but thus far, whether its mechanism of action is achieved by regulating the intestinal flora has not been elucidated.

METHODS

In this study, chronic unpredictable mild stress (CUMS) along with isolated feeding created a rat depression model, and YJW was administered for intervention. Rats were put through behavioral tests to determine their level of depression, and ELISA was utilized for measuring the level of monoamine neurotransmitters (MNTs) in the hippocampus. Metagenomic gene sequencing analysis was used to study the effect of depression on the intestinal flora in rats and the regulatory mechanism of YJW on the intestinal flora. Furthermore, ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) was utilized for fecal metabolomics studies to further reveal the antidepressant mechanism of YJW. The antidepressant mechanism of YJW was explored and further verified by Western blot analysis.

RESULTS

Different doses of YJW improved the depressive state of rats and raised the levels of MNTs in the hippocampus. The results of metagenomic sequencing indicated that the YJW recovered the structure and diversity of the intestinal flora in depressed rats. Metabolomics revealed sustained changes in 21 metabolites after the treatment of YJW, suggesting that YJW can play an antidepressant role by improving abnormal metabolic pathways. The results of correlation analysis suggested that YJW might mediate Eubacterium, Oscillibacter, Roseburia, Romboutsia and Bacterium to regulate purine metabolism, tryptophan metabolism, primary bile acid biosynthesis, and glutamate metabolism and exert antidepressant effects. Western blot analysis showed that YJW reduced the content of IL-1β in the hippocampus, inhibited the activation of the NLRP3 inflammasome in the hippocampus of rats, and increased the content of ZO-1 in the colon of rats.

CONCLUSION

YJW can alleviate depressive symptoms in depressed rats, and its mechanism is connected to improving intestinal flora and regulating body metabolism.

Global Profiling of Urinary Mercapturic Acids Using Integrated Library-Guided Analysis

Urinary mercapturic acids (MAs) are often used as biomarkers for monitoring human exposures to occupational and environmental xenobiotics. In this study, we developed an integrated library-guided analysis workflow using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. This method includes expanded assignment criteria and a curated library of 220 MAs and addresses the shortcomings of previous untargeted approaches. We employed this workflow to profile MAs in the urine of 70 participants─40 nonsmokers and 30 smokers. We found approximately 500 MA candidates in each urine sample, and 116 MAs from 63 precursors were putatively annotated. These include 25 previously unreported MAs derived mostly from alkenals and hydroxyalkenals. Levels of 68 MAs were comparable in nonsmokers and smokers, 2 MAs were higher in nonsmokers, and 46 MAs were elevated in smokers. These included MAs of polycyclic aromatic hydrocarbons and hydroxyalkenals and those derived from toxicants present in cigarette smoke (e.g., acrolein, 1,3-butadiene, isoprene, acrylamide, benzene, and toluene). Our workflow allowed profiling of known and unreported MAs from endogenous and environmental sources, and the levels of several MAs were increased in smokers. Our method can also be expanded and applied to other exposure-wide association studies.

Cell Metabolomics Study on Synergistic anti-Hepatocellular Carcinoma Effect of Aidi Injection Combined with Doxorubicin

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the second most common cause of cancer deaths. This study aimed to explore the inhibitory effect and mechanism of Aidi injection (ADI) combined with doxorubicin (DOX) in HCC treatment. The drug concentrations in combined threapy was determined by investigating the effect of various concentrations of ADI and DOX on the viability of H22 cells. The combination index (CI) was also calculated. A metabolomic strategy based on ultrahigh performance liquid chromatography tandem mass spectrometry (UHPLC-MS/MS) platform was established to analyze the metabolites. As a result, the CI values were less than 1, indicating that the combination of ADI and DOX exerted a synergistic effect on HCC treatment. The combination of 40‰ ADI and 1 μmol/L DOX had the strongest inhibitory effect and was used for subsequent metabolomic analysis. A total of 19 metabolic markers were obtained in metabolomic analysis, including amino acids (L-glutamic acid, L-arginine, and L-tyrosine), organic acids (succinic acid and citric acid), adenosine, and hypoxanthine , etc. Compared with the treatment using DOX or ADI alone, the combined therapy further regulated the levels of metabolic markers in HCC, which may be the reason for the synergistic effect. Seven metabolic pathways were significantly enriched, including phenylalanine, tyrosine and tryptophan biosynthesis, D-glutamine and D-glutamate metabolism, alanine, aspartate and glutamate metabolism, phenylalanine metabolism, arginine biosynthesis, tricarboxylic acid (TCA) cycle, and purine metabolism. These findings demonstrated that ADI combined with DOX can effectively inhibit the viability of H22 cells, which may exert a synergistic anti-tumor effect by balancing the metabolism of amino acids and energy-related substances.

Alpha-Glucosidase Inhibitory Effect of Psychotria malayana Jack Leaf: A Rapid Analysis Using Infrared Fingerprinting

The plant Psychotria malayana Jack belongs to the Rubiaceae family and is known in Malaysia as “meroyan sakat/salung”. A rapid analytical technique to facilitate the evaluation of the P. malayana leaves’ quality has not been well-established yet. This work aimed therefore to develop a validated analytical technique in order to predict the alpha-glucosidase inhibitory action (AGI) of P. malayana leaves, applying a Fourier Transform Infrared Spectroscopy (FTIR) fingerprint and utilizing an orthogonal partial least square (OPLS). The dried leaf extracts were prepared by sonication of different ratios of methanol-water solvent (0, 25, 50, 75, and 100% v/v) prior to the assessment of alpha-glucosidase inhibition (AGI) and the following infrared spectroscopy. The correlation between the biological activity and the spectral data was evaluated using multivariate data analysis (MVDA). The 100% methanol extract possessed the highest inhibitory activity against the alpha-glucosidase (IC₅₀ 2.83 ± 0.32 μg/mL). Different bioactive functional groups, including hydroxyl (O-H), alkenyl (C=C), methylene (C-H), carbonyl (C=O), and secondary amine (N-H) groups, were detected by the multivariate analysis. These functional groups actively induced the alpha-glucosidase inhibition effect. This finding demonstrated the spectrum profile of the FTIR for the natural herb P. malayana Jack, further confirming its medicinal value. The developed validated model can be used to predict the AGI of P. malayana, which will be useful as a tool in the plant’s quality control.

Integration of metabolomics and proteomics to reveal the metabolic characteristics of high-intensity interval training

Metabolomics and proteomics were integrated to research the molecular characterization of high-intensity interval training, revealing changes in biological pathways.

Liver metabolomic characterization of Sophora flavescens alcohol extract-induced hepatotoxicity in rats through UPLC/LTQ-Orbitrap mass spectrometry

This study aimed to observe the influence of Sophora flavescens alcohol extract (SFAE) on hepatic metabolic profiling in rats to explore the possible mechanism of hepatotoxicity induced by S. flavescens.Male Sprague-Dawley rats were randomly divided into three groups (n = 6 in each group) and administered with SFAE at different doses of 0, 1.25 and 2.5 g/kg for two weeks. Ultra-performance liquid chromatography-high resolution mass spectrometry was utilized to detect the change in the metabolites in rat liver. Principal component analysis and orthogonal partial least squares discriminant analysis were adapted to perform multivariate statistical analysis between groups and to screen the potential biomarkers. Related metabolic pathway analysis was also conducted.Results indicated that hepatic metabolites in the three groups were separated on day 14, and 25 major differential metabolites were identified. Six bile acids, four carnitines, four lysophosphatidylcholines and glutathione were closely related to hepatotoxicity. Liver metabolomic results showed that rats orally exposed to SFAE exhibited a disturbance of the metabolism of bile acids, fatty acids, glycerophospholipids and amino acids.This study provided new insights into the possible mechanism of hepatotoxicity induced by SFAE in rats.

Discovering biomarkers in bladder cancer by metabolomics

It has become increasingly clear that the development of cancer, a multifactorial disease, cannot be explained by a single molecule or gene mutation. As a new discipline, metabolomics focuses on the body's metabolite changes, and attempts to find differences to explain the development of cancer; it has proven to be effective and credible. Metabolic studies of bladder cancer (BCa) lag behind those of other tumors. This review systematically outlines the specific process of metabolomics and the use of metabolomics in BCa studies in recent years. We have reviewed the in vitro cell line, bladder tumor tissue and biofluid (urine, plasma and serum) studies used in metabolomics analyses of BCa. The advantages and drawbacks of the use of different samples were compared. Based on the available studies, we have further described the aberrant metabolic pathways of BCa and have suggested some metabolites that may be potential biomarkers for BCa detection.

Metabolome analysis for investigating host-gut microbiota interactions

Dysbiosis of the gut microbiome is associated with host health conditions. Many diseases have shown to have correlations with imbalanced microbiota, including obesity, inflammatory bowel disease, cancer, and even neurodegeneration disorders. Metabolomics studies targeting small molecule metabolites that impact the host metabolome and their biochemical functions have shown promise for studying host-gut microbiota interactions. Metabolome analysis determines the metabolites being discussed for their biological implications in host-gut microbiota interactions. To facilitate understanding the critical aspects of metabolome analysis, this article reviewed (1) the sample types used in host-gut microbiome studies; (2) mass spectrometry (MS)-based analytical methods and (3) useful tools for MS-based data processing/analysis. In addition to the most frequently used sample type, feces, we also discussed others biosamples, such as urine, plasma/serum, saliva, cerebrospinal fluid, exhaled breaths, and tissues, to better understand gut metabolite systemic effects on the whole organism. Gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), three powerful tools that can be utilized to study host-gut microbiota interactions, are included with examples of their applications. After obtaining big data from MS-based instruments, noise removal, peak detection, missing value imputation, and data analysis are all important steps for acquiring valid results in host-gut microbiome research. The information provided in this review will help new researchers aiming to join this field by providing a global view of the analytical aspects involved in gut microbiota-related metabolomics studies.

Biomarkers of exposure to new and emerging tobacco delivery products

Accurate and reliable measurements of exposure to tobacco products are essential for identifying and confirming patterns of tobacco product use and for assessing their potential biological effects in both human populations and experimental systems. Due to the introduction of new tobacco-derived products and the development of novel ways to modify and use conventional tobacco products, precise and specific assessments of exposure to tobacco are now more important than ever. Biomarkers that were developed and validated to measure exposure to cigarettes are being evaluated to assess their use for measuring exposure to these new products. Here, we review current methods for measuring exposure to new and emerging tobacco products, such as electronic cigarettes, little cigars, water pipes, and cigarillos. Rigorously validated biomarkers specific to these new products have not yet been identified. Here, we discuss the strengths and limitations of current approaches, including whether they provide reliable exposure estimates for new and emerging products. We provide specific guidance for choosing practical and economical biomarkers for different study designs and experimental conditions. Our goal is to help both new and experienced investigators measure exposure to tobacco products accurately and avoid common experimental errors. With the identification of the capacity gaps in biomarker research on new and emerging tobacco products, we hope to provide researchers, policymakers, and funding agencies with a clear action plan for conducting and promoting research on the patterns of use and health effects of these products.

QGQS Granule in SHR Serum Metabonomics Study Based on Tools of UPLC-Q-TOF and Renin-Angiotensin-Aldosterone System Form Protein Profilin-1

QGQS granule is effective for the therapeutic of hypertension in clinic. The aim of this research is to observe the antihypertension effect of QGQS granule on SHR and explain the mechanism of its lowering blood pressure. 30 SHR were selected as model group, captopril group, and QGQS group, 10 WKYr were used as control group, and RBP were measured on tail artery consciously. And all the serum sample analysis was carried out on UPLC-TOF-MS system to determine endogenous metabolites and to find the metabonomics pathways. Meanwhile, ELISA kits for the determination pharmacological indexes of PRA, AngI, AngII, and ALD were used for pathway confirmatory; WB for determination of profilin-1 protein expression was conducted for Ang II pathway analysis as well. It is demonstrated that QGQS granule has an excellent therapeutic effect on antihypertension, which exerts effect mainly on metabonomics pathway by regulating glycerophospholipid, sphingolipid, and arachidonic acid metabolism, and it could inhibit the overexpression of the profilin-1 protein. We can come to a conclusion that RAAS should be responsible mainly for the metabonomics pathway of QGQS granule on antihypertension, and it plays a very important role in protein of profilin-1 inhibition.

NMR and MS Methods for Metabolomics

Metabolomics, also often referred as "metabolic profiling," is the systematic profiling of metabolites in biofluids or tissues of organisms and their temporal changes. In the last decade, metabolomics has become more and more popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabolomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabolomics, i.e., NMR, UPLC-MS, and GC-MS, have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabolomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation to determining the measurement details of all analytical platforms, and finally to discussing the corresponding specific steps of data analysis.

Development of a Rapid Microbore Metabolic Profiling Ultraperformance Liquid Chromatography-Mass Spectrometry Approach for High-Throughput Phenotyping Studies

A rapid gradient microbore ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) method has been developed to provide a high-throughput analytical platform for the metabolic phenotyping of urine from large sample cohorts. The rapid microbore metabolic profiling (RAMMP) approach was based on scaling a conventional reversed-phase UPLC-MS method for urinary profiling from 2.1 mm × 100 mm columns to 1 mm × 50 mm columns, increasing the linear velocity of the solvent, and decreasing the gradient time to provide an analysis time of 2.5 min/sample. Comparison showed that conventional UPLC-MS and rapid gradient approaches provided peak capacities of 150 and 50, respectively, with the conventional method detecting approximately 19 000 features compared to the ∼6 000 found using the rapid gradient method. Similar levels of repeatability were seen for both methods. Despite the reduced peak capacity and the reduction in ions detected, the RAMMP method was able to achieve similar levels of group discrimination as conventional UPLC-MS when applied to rat urine samples obtained from investigative studies on the effects of acute 2-bromophenol and chronic acetaminophen administration. When compared to a direct infusion MS method of similar analysis time the RAMMP method provided superior selectivity. The RAMMP approach provides a robust and sensitive method that is well suited to high-throughput metabonomic analysis of complex mixtures such as urine combined with a 5-fold reduction in analysis time compared with the conventional UPLC-MS method.

The Progress of Metabolomics Study in Traditional Chinese Medicine Research

Traditional Chinese medicine (TCM) has played important roles in health protection and disease treatment for thousands of years in China and has gained the gradual acceptance of the international community. However, many intricate issues, which cannot be explained by traditional methods, still remain, thus, new ideas and technologies are needed. As an emerging system biology technology, the holistic view adopted by metabolomics is similar to that of TCM, which allows us to investigate TCM with complicated conditions and multiple factors in depth. In this paper, we tried to give a timely and comprehensive update about the methodology progression of metabolomics, as well as its applications, in different fields of TCM studies including quality control, processing, safety and efficacy evaluation. The herbs investigated by metabolomics were selected for detailed examination, including Anemarrhena asphodeloides Bunge, Atractylodes macrocephala Kidd, Pinellia ternate, etc.; furthermore, some valuable results have been obtained and summarized. In conclusion, although the study of metabolomics is at the early phase and requires further scrutiny and validation, it still provides bright prospects to dissect the synergistic action of multiple components from TCM. Overall, with the further development of analytical techniques, especially multi-analysis techniques, we expect that metabolomics will greatly promote TCM research and the establishment of international standards, which is beneficial to TCM modernization.

Global metabolite profiling and diagnostic ion filtering strategy by LC-QTOF MS for rapid identification of raw and processed pieces of Rheum palmatum L

Due to its variety of functions, rhubarb has been used for thousands of years in many countries. It is commonly used after processing. Processing usually affect the chemical profile and the contents of active compounds in herbals, leading to changes of their bioactivities. Here, an approach of metabolite profiling and diagnostic ion filtering strategy with liquid chromatography-quadrupole/time-of-flight mass spectrometry was established for rapid identification of raw and processed pieces of Rheum palmatum L. (RPL). The comprehensive and unbiased information of 30 batches of RPL covering raw and two general processing methods were given by metabolomic profiles. Using molecular feature extraction algorithm, non-targeted compounds were analyzed in minutes. In total, 73 characteristic markers were extracted and identified by diagnostic ion filtering. They have been further analyzed by partial least squares-support vector machine-based pattern recognition. The comprehensive and rapid method for raw and processed pieces of RPL classification shows good sensitivity, specificity and prediction performance.

Application of GC-MS coupled with chemometrics for scanning serum metabolic biomarkers from renal fibrosis rat

Renal interstitial fibrosis closely relates to chronic kidney disease and is regarded as the final common pathway in most cases of end-stage renal disease. Metabolomic biomarkers can facilitate early diagnosis and allow better understanding of the pathogenesis underlying renal fibrosis. Gas chromatography-mass spectrometry (GC/MS) is one of the most promising techniques for identification of metabolites. However, the existence of the background, baseline offset, and overlapping peaks makes accurate identification of the metabolites unachievable. In this study, GC/MS coupled with chemometric methods was successfully developed to accurately identify and seek metabolic biomarkers for rats with renal fibrosis. By using these methods, seventy-six metabolites from rat serum were accurately identified and five metabolites (i.e., urea, ornithine, citric acid, galactose, and cholesterol) may be useful as potential biomarkers for renal fibrosis.

Lipidomic analysis of plasma, erythrocytes and lipoprotein fractions of cardiovascular disease patients using UHPLC/MS, MALDI-MS and multivariate data analysis

Differences among lipidomic profiles of healthy volunteers, obese people and three groups of cardiovascular disease (CVD) patients are investigated with the goal to differentiate individual groups based on the multivariate data analysis (MDA) of lipidomic data from plasma, erythrocytes and lipoprotein fractions of more than 50 subjects. Hydrophilic interaction liquid chromatography on ultrahigh-performance liquid chromatography (HILIC-UHPLC) column coupled with electrospray ionization mass spectrometry (ESI-MS) is used for the quantitation of four classes of polar lipids (phosphatidylethanolamines, phosphatidylcholines, sphingomyelins and lysophosphatidylcholines), normal-phase UHPLC-atmospheric pressure chemical ionization MS (NP-UHPLC/APCI-MS) is applied for the quantitation of five classes of nonpolar lipids (cholesteryl esters, triacylglycerols, sterols, 1,3-diacylglycerols and 1,2-diacylglycerols) and the potential of matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is tested for the fast screening of all lipids without a chromatographic separation. Obtained results are processed by unsupervised (principal component analysis) and supervised (orthogonal partial least squares) MDA approaches to highlight the largest differences among individual groups and to identify lipid molecules with the highest impact on the group differentiation.

Toxicity of Glutathione-Binding Metals: A Review of Targets and Mechanisms

Mercury, cadmium, arsenic and lead are among priority metals for toxicological studies due to the frequent human exposure and to the significant burden of disease following acute and chronic intoxication. Among their common characteristics is chemical affinity to proteins and non-protein thiols and their ability to generate cellular oxidative stress by the best-known Fenton mechanism. Their health effects are however diverse: kidney and liver damage, cancer at specific sites, irreversible neurological damages with metal-specific features. Mechanisms for the induction of oxidative stress by interaction with the cell thiolome will be presented, based on literature evidence and of experimental findings.

Liquid chromatography-mass spectrometry of biofluids and extracts

Metabonomics aims at a comprehensive and semiquantitative monitoring of the perturbations of the metabonome in response to pathophysiological stimuli. Liquid chromatography-mass spectrometry (LC-MS) is one of the most commonly employed analytical platforms for this purpose. As the sample pretreatment is detailed in the former chapter, we here describe the practical procedures for the LC-MS-based metabolic profiling of biological samples including detailed liquid chromatographic and MS conditions, batch analysis, peak alignment, data quality assessment, and compound identification.

A modified data normalization method for GC-MS-based metabolomics to minimize batch variation

The goal of metabolomics data pre-processing is to eliminate systematic variation, such that biologically-related metabolite signatures are detected by statistical pattern recognition. Although several methods have been developed to tackle the issue of batch-to-batch variation, each method has its advantages and disadvantages. In this study, we used a reference sample as a normalization standard for test samples within the same batch, and each metabolite value is expressed as a ratio relative to its counterpart in the reference sample. We then applied this approach to a large multi-batch data set to facilitate intra- and inter-batch data integration. Our results demonstrate that normalization to a single reference standard has the potential to minimize batch-to-batch data variation across a large, multi-batch data set.

A generic multiple reaction monitoring based approach for plant flavonoids profiling using a triple quadrupole linear ion trap mass spectrometry

Flavonoids are one of the largest classes of plant secondary metabolites serving a variety of functions in plants and associating with a number of health benefits for humans. Typically, they are co-identified with many other secondary metabolites using untargeted metabolomics. The limited data quality of untargeted workflow calls for a shift from the breadth-first to the depth-first screening strategy when a specific biosynthetic pathway is focused on. Here we introduce a generic multiple reaction monitoring (MRM)-based approach for flavonoids profiling in plants using a hybrid triple quadrupole linear ion trap (QTrap) mass spectrometer. The approach includes four steps: (1) preliminary profiling of major aglycones by multiple ion monitoring triggered enhanced product ion scan (MIM-EPI); (2) glycones profiling by precursor ion triggered EPI scan (PI-EPI) of major aglycones; (3) comprehensive aglycones profiling by combining MIM-EPI and neutral loss triggered EPI scan (NL-EPI) of major glycone; (4) in-depth flavonoids profiling by MRM-EPI with elaborated MRM transitions. Particularly, incorporation of the NH3 loss and sugar elimination proved to be very informative and confirmative for flavonoids screening. This approach was applied for profiling flavonoids in Astragali radix (Huangqi), a famous herb widely used for medicinal and nutritional purposes in China. In total, 421 flavonoids were tentatively characterized, among which less than 40 have been previously reported in this medicinal plant. This MRM-based approach provides versatility and sensitivity that required for flavonoids profiling in plants and serves as a useful tool for plant metabolomics.

Ion fusion of high-resolution LC-MS-based metabolomics data to discover more reliable biomarkers

A systematic approach for the fusion of associated ions from a common molecule was developed to generate "one feature for one peak" metabolomics data. This approach guarantees that each molecule is equally selected as a potential biomarker and may largely enhance the chance to obtain reliable findings without employing redundant ion information. The ion fusion is based on low mass variation in contrast to the theoretical calculation measured by a high-resolution mass spectrometer, such as LTQ orbitrap, and a high correlation of ion pairs from the same molecule. The mass characteristics of isotopic distribution, neutral loss, and adduct ions were simultaneously applied to inspect each extracted ion in the range of a predefined retention time window. The correlation coefficient was computed with the corresponding intensities of each ion pair among all experimental samples. Serum metabolomics data for the investigation of hepatocellular carcinoma (HCC) and healthy controls were utilized as an example to demonstrate this strategy. In total, 609 and 1084 ion pairs were respectively found meeting one or more criteria for fusion, and therefore fused to 106 and 169 metabolite features of the datasets in the positive and negative modes, respectively. The important metabolite features were separately discovered and compared to distinguish the HCC from the healthy controls using the two datasets with and without ion fusion. The results show that the developed method can be an effective tool to process high-resolution mass spectrometry data in "omics" studies.

Batch profiling calibration for robust NMR metabonomic data analysis

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up. We show how this method can be applied to data calibration in the context of a large-scale NMR epidemiological study where quality control samples are available. We also illustrate the efficiency of a batch profile correction for NMR metabonomic investigation of cell extracts, where GBP can significantly improve the predictive power of multivariate statistical models for discriminant analysis of the cell infection status. The method is applicable to a broad range of NMR metabolomic/metabonomic cohort studies.

From numbers to a biological sense: How the strategy chosen for metabolomics data treatment may affect final results. A practical example based on urine fingerprints obtained by LC-MS

Application of high-throughput technologies in metabolomics studies increases the quantity of data obtained, which in turn imposes several problems during data analysis. Correctly and clearly addressed biological question and comprehensive knowledge about data structure and properties are definitely necessary to select proper chemometric tools. However, there is a broad range of chemometric tools available for use with metabolomics data, which makes this choice challenging. Precisely performed data treatment enables valuable extraction of information and its proper interpretation. The effect of an error made at an early stage will be enhanced throughout the later stages, which in combination with other errors made at each step can accumulate and significantly affect the data interpretation. Moreover, adequate application of these tools may help not only to detect, but sometimes also to correct, biological, analytical, or methodological errors, which may affect truthfulness of obtained results. This report presents steps and tools used for LC-MS based metabolomics data extraction, reduction, and visualization. Following such steps as data reprocessing, data pretreatment, data treatment, and data revision, authors want to show how to extract valuable information and how to avoid misinterpretation of results obtained. The purpose of this work was to emphasize problematic characteristics of metabolomics data and the necessity for their attentive and precise treatment. The dataset used to illustrate metabolomics data properties and to illustrate major data treatment challenges was obtained utilizing an animal model of control and diabetic rats, both with and without rosemary treatment. Urine samples were fingerprinted employing LC-QTOF-MS.

Application of metabolomics approaches to the study of respiratory diseases

Metabolomics is the global unbiased analysis of all the small-molecule metabolites within a biological system, under a given set of conditions. These methods offer the potential for a holistic approach to clinical medicine, as well as improving disease diagnosis and understanding of pathological mechanisms. Respiratory diseases including asthma and chronic obstructive pulmonary disorder are increasing globally, with the latter predicted to become the third leading cause of global mortality by 2020. The root causes for disease onset remain poorly understood and no cures are available. This review presents an overview of metabolomics followed by in-depth discussion of its application to the study of respiratory diseases, including the design of metabolomics experiments, choice of clinical material collected and potentially confounding experimental factors. Particular challenges in the field are presented and placed within the context of the future of the applications of metabolomics approaches to the study of respiratory diseases.

Adductomics: characterizing exposures to reactive electrophiles

To understand environmental causes of disease, unbiased methods are needed to characterize the human exposome, which represents all toxicants to which people are exposed from both exogenous and endogenous sources. Because they directly modify DNA and important proteins, reactive electrophiles are probably the most important constituents of the exposome. Exposures to reactive electrophiles can be characterized by measuring adducts from reactions between circulating electrophiles and blood nucleophiles. We define an 'adductome' as the totality of such adducts with a given nucleophilic target. Because of their greater abundance and residence times in human blood, adducts of hemoglobin (Hb) and human serum albumin (HSA) are preferable to those of DNA and glutathione for characterizing adductomes. In fact, the nucleophilic hotspot represented by the only free sulfhydryl group in HSA (HSA-Cys(34)) offers particular advantages for adductomic experiments. Although targeted adducts of HSA-Cys(34) have been monitored for decades, an unbiased method has only recently been reported for visualizing the HSA-Cys(34) 'subadductome'. The method relies upon a novel mass spectrometry application, termed fixed-step selected reaction monitoring (FS-SRM), to profile Cys(34) adducts in tryptic digests of HSA. Here, we selectively review the literature regarding the potential of adductomics to partially elucidate the human exposome, with particular attention to the HSA-Cys(34) subadductome.

[Effects of Chinese herbal medicine Huanglian Jiedu Decoction on urine metabonomics of healthy people]

OBJECTIVE

To study biomarkers and their diversification in healthy people's urine before and after oral administration of Huanglian Jiedu Decoction, a compound Chinese herbal medicine, and to investigate the influence of Huanglian Jiedu Decoction on urine metabonomics of healthy people.

METHODS

Ten healthy volunteers joined in this experiment and signed informed consent. They were given oral administration of Huanglian Jiedu Decoction for 3 days, and their urine was collected every morning. The samples were centrifuged at 13 000×g for 10 min at 4 degrees centigrade, and the supernatant was stored at -75 degrees centigrade. The samples were examined by a core technology called ultra-performance liquid chromatography quadruple time of flight/mass spectrometry and then the data were analyzed by principal components analysis and partial least square-discriminant analysis to investigate trajectory diversification and corresponding relations of the time and amount of urine metabonomics before and after oral administration of Huanglian Jiedu Decoction.

RESULTS

Evident variance of urine metabonomics took place after oral administration of Huanglian Jiedu Decoction, which showed that the metabolism of healthy people had been visibly affected. Seven potential biomarkers which play a role in healthy people on behalf of Huanglian Jiedu Decoction were preliminarily found, and one biomarker which had biological significance was identified as 2-(formylamino)-benzoic acid.

CONCLUSION

The effects of Huanglian Jiedu Decoction on urine metabonomics in healthy people investigated at the level of metabolism can provide theoretical and technical support for the study of Huanglian Jiedu Decoction's influence on heat syndrome.

NMR and MS methods for metabonomics

Metabonomics, also often referred to as "metabolomics" or "metabolic profiling," is the systematic profiling of metabolites in bio-fluids or tissues of organisms and their temporal changes. In the last decade, metabonomics has become increasingly popular in drug development, molecular medicine, and other biotechnology fields, since it profiles directly the phenotype and changes thereof in contrast to other "-omics" technologies. The increasing popularity of metabonomics has been possible only due to the enormous development in the technology and bioinformatics fields. In particular, the analytical technologies supporting metabonomics, i.e., NMR, LC-MS, UPLC-MS, and GC-MS have evolved into sensitive and highly reproducible platforms allowing the determination of hundreds of metabolites in parallel. This chapter describes the best practices of metabonomics as seen today. All important steps of metabolic profiling in drug development and molecular medicine are described in great detail, starting from sample preparation, to determining the measurement details of all analytical platforms, and finally, to discussing the corresponding specific steps of data analysis.

Profiling Cys34 adducts of human serum albumin by fixed-step selected reaction monitoring

A method is described for profiling putative adducts (or other unknown covalent modifications) at the Cys34 locus of human serum albumin (HSA), which represents the preferred reaction site for small electrophilic species in human serum. By comparing profiles of putative HSA-Cys34 adducts across populations of interest it is theoretically possible to explore environmental causes of degenerative diseases and cancer caused by both exogenous and endogenous chemicals. We report a novel application of selected-reaction-monitoring (SRM) mass spectrometry, termed fixed-step SRM (FS-SRM), that allows detection of essentially all HSA-Cys34 modifications over a specified range of mass increases (added masses). After tryptic digestion, HSA-Cys34 adducts are contained in the third largest peptide (T3), which contains 21 amino acids and an average mass of 2433.87 Da. The FS-SRM method does not require that exact masses of T3 adducts be known in advance but rather uses a theoretical list of T3-adduct m/z values separated by a fixed increment of 1.5. In terms of added masses, each triply charged parent ion represents a bin of ±2.3 Da between 9.1 Da and 351.1 Da. Synthetic T3 adducts were used to optimize FS-SRM and to establish screening rules based upon selected b- and y-series fragment ions. An isotopically labeled T3 adduct is added to protein digests to facilitate quantification of putative adducts. We used FS-SRM to generate putative adduct profiles from six archived specimens of HSA that had been pooled by gender, race, and smoking status. An average of 66 putative adduct hits (out of a possible 77) were detected in these samples. Putative adducts covered a wide range of concentrations, were most abundant in the mass range below 100 Da, and were more abundant in smokers than in nonsmokers. With minor modifications, the FS-SRM methodology can be applied to other nucleophilic sites and proteins.

Development and validation of a liquid chromatography-mass spectrometry metabonomic platform in human plasma of liver failure caused by hepatitis B virus

This paper presents an liquid chromatography (LC)/mass spectrometry (MS)-based metabonomic platform that combined the discovery of differential metabolites through principal component analysis (PCA) with the verification by selective multiple reaction monitoring (MRM). These methods were applied to analyze plasma samples from liver disease patients and healthy donors. LC-MS raw data (about 1000 compounds), from the plasma of liver failure patients (n = 26) and healthy controls (n = 16), were analyzed through the PCA method and a pattern recognition profile that had significant difference between liver failure patients and healthy controls (P < 0.05) was established. The profile was verified in 165 clinical subjects. The specificity and sensitivity of this model in predicting liver failure were 94.3 and 100.0%, respectively. The differential ions with m/z of 414.5, 432.0, 520.5, and 775.0 were verified to be consistent with the results from PCA by MRM mode in 40 clinical samples, and were proved not to be caused by the medicines taken by patients through rat model experiments. The compound with m/z of 520.5 was identified to be 1-Linoleoylglycerophosphocholine or 1-Linoleoylphosphatidylcholine through exact mass measurements performed using Ion Trap-Time-of-Flight MS and METLIN Metabolite Database search. In all, it was the first time to integrate metabonomic study and MRM relative quantification of differential peaks in a large number of clinical samples. Thereafter, a rat model was used to exclude drug effects on the abundance of differential ion peaks. 1-Linoleoylglycerophosphocholine or 1-Linoleoylphosphatidylcholine, a potential biomarker, was identified. The LC/MS-based metabonomic platform could be a powerful tool for the metabonomic screening of plasma biomarkers.

Equating, or correction for between-block effects with application to body fluid LC-MS and NMR metabolomics data sets

Combination of data sets from different objects (for example, from two groups of healthy volunteers from the same population) that were measured on a common set of variables (for example, metabolites or peptides) is desirable for statistical analysis in "omics" studies because it increases power. However, this type of combination is not directly possible if nonbiological systematic differences exist among the individual data sets, or "blocks". Such differences can, for example, be due to small analytical changes that are likely to accumulate over large time intervals between blocks of measurements. In this article we present a data transformation method, that we will refer to as "quantile equating", which per variable corrects for linear and nonlinear differences in distribution among blocks of semiquantitative data obtained with the same analytical method. We demonstrate the successful application of the quantile equating method to data obtained on two typical metabolomics platforms, i.e., liquid chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. We suggest uni- and multivariate methods to evaluate similarities and differences among data blocks before and after quantile equating. In conclusion, we have developed a method to correct for nonbiological systematic differences among semiquantitative data blocks and have demonstrated its successful application to metabolomics data sets.

Analysis of endogenous glutathione-adducts and their metabolites

The ability to conduct validated analyses of glutathione (GSH)-adducts and their metabolites is critically important in order to establish whether they play a role in cellular biochemical or pathophysiological processes. The use of stable isotope dilution (SID) methodology in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides the highest bioanalytical specificity possible for such analyses. Quantitative studies normally require the high sensitivity that can be obtained by the use of multiple reaction monitoring (MRM)/MS rather than the much less sensitive but more specific full scanning methodology. The method employs a parent ion corresponding to the intact molecule together with a prominent product ion that obtained by collision induced dissociation. Using SID LC-MRM/MS, analytes must have the same relative LC retention time to the heavy isotope internal standard established during the validation procedure, the correct parent ion and the correct product ion. This level of specificity cannot be attained with any other bioanalytical technique employed for biomarker analysis. This review will describe the application of SID LC-MR/MS methodology for the analysis of GSH-adducts and their metabolites. It will also discuss potential future directions for the use of this methodology for rigorous determination of their utility as disease and exposure biomarkers.

Opening up the "Black Box": metabolic phenotyping and metabolome-wide association studies in epidemiology

BACKGROUND

Metabolic phenotyping of humans allows information to be captured on the interactions between dietary, xenobiotic, other lifestyle and environmental exposures, and genetic variation, which together influence the balance between health and disease risks at both individual and population levels.

OBJECTIVES

We describe here the main procedures in large-scale metabolic phenotyping and their application to metabolome-wide association (MWA) studies.

METHODS

By use of high-throughput technologies and advanced spectroscopic methods, application of metabolic profiling to large-scale epidemiologic sample collections, including metabolome-wide association (MWA) studies for biomarker discovery and identification.

DISCUSSION

Metabolic profiling at epidemiologic scale requires optimization of experimental protocol to maximize reproducibility, sensitivity, and quantitative reliability, and to reduce analytical drift. Customized multivariate statistical modeling approaches are needed for effective data visualization and biomarker discovery with control for false-positive associations since 100s or 1,000s of complex metabolic spectra are being processed.

CONCLUSION

Metabolic profiling is an exciting addition to the armamentarium of the epidemiologist for the discovery of new disease-risk biomarkers and diagnostics, and to provide novel insights into etiology, biological mechanisms, and pathways.

Combination of 1H nuclear magnetic resonance spectroscopy and liquid chromatography/mass spectrometry with pattern recognition techniques for evaluation of metabolic profile associated with albuminuria

A method using 1H NMR and LC/MS with pattern recognition tools such as principal component analysis (PCA) and orthogonal projection to latent structure discriminant analysis (O-PLS-DA) was used to study the urinary metabolic profiles associated with an increase in urinary albumin in a general population. The normalized peak intensities obtained from 1H NMR and LC/MS with nonparametric two-tailed Mann-Whitney analysis was used for the identification of network of potential biomarkers corresponding to the increase of albumin in urine. The specificity of detecting the stated metabolites by 1H NMR and LC/MS was demonstrated. Our preliminary data obtained demonstrated that LC/MS may produce more distinctive metabolic profiles. For the patient group, changes in alanine, kyneurnic acid, and xanthurenic acid might be associated with changes in the tryptophan metabolism. At the same time, other metabolites that were involved in citric acid cycle, amino acid metabolism, and cellular functions were affected in the patient group. The proposed approach provided a comprehensive picture of the metabolic changes induced by the increase of protein in urine and demonstrated the advantages of using multiple diagnostic biomarkers. At the same time, the current work was demonstrated as a potential cost-effective solution of high-throughput analysis with pattern recognition tools as applied here in a real clinical situation.

Mechanism-based bioanalysis and biomarkers for hepatic chemical stress

Adverse drug reactions, in particular drug-induced hepatotoxicity, represent a major challenge for clinicians and an impediment to safe drug development. Novel blood or urinary biomarkers of chemically-induced hepatic stress also hold great potential to provide information about pathways leading to cell death within tissues. The earlier pre-clinical identification of potential hepatotoxins and non-invasive diagnosis of susceptible patients, prior to overt liver disease is an important goal. Moreover, the identification, validation and qualification of biomarkers that have in vitro, in vivo and clinical transferability can assist bridging studies and accelerate the pace of drug development. Drug-induced chemical stress is a multi-factorial process, the kinetics of the interaction between the hepatotoxin and the cellular macromolecules are crucially important as different biomarkers will appear over time. The sensitivity of the bioanalytical techniques used to detect biological and chemical biomarkers underpins the usefulness of the marker in question. An integrated analysis of the biochemical, molecular and cellular events provides an understanding of biological (host) factors which ultimately determine the balance between xenobiotic detoxification, adaptation and liver injury. The aim of this review is to summarise the potential of novel mechanism-based biomarkers of hepatic stress which provide information to connect the intracellular events (drug metabolism, organelle, cell and whole organ) ultimately leading to tissue damage (apoptosis, necrosis and inflammation). These biomarkers can provide both the means to inform the pharmacologist and chemist with respect to safe drug design, and provide clinicians with valuable tools for patient monitoring.